2nd International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD, 2008)
24th August 2008, Las Vegas, NV URL: http://www.ornl.gov/sci/knowledgediscovery/SensorKDD-2008/index.htm In conjunction with ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08) 24-27 August 2008, Las Vegas, NV. Important dates * May 28, 2008 * June 15, 2008: Author notification * June 20, 2008: Submission of Camera-ready papers (ACM hard deadline) * August 24, 2008: Full-day Workshop at ACM SIGKDD Conference, Las Vegas, USA Brief Description Wide-area sensor infrastructures, remote sensors, and wireless sensor networks, RFIDs, yield massive volumes of disparate, dynamic, and geographically distributed data. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including disaster preparedness and management, adaptability to climate change, national or homeland security, and the management of critical infrastructures. The raw data from sensors need to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or human-induced tactical decisions or strategic policy based on decision sciences and decision support systems. The challenges for the knowledge discovery community are expected to be immense. On the one hand, dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating offline predictive insights, which in turn can facilitate real-time analysis. Problems ranging from mitigating hurricane impacts, preparing for abrupt climate change, preventing terror attacks and monitoring improvised explosive devices require knowledge discovery solutions designed to detect and analyze anomalies, change, extremes and nonlinear processes, and departures from normal behavior. In order to be relevant to society, solutions must eventually reach end-to-end, covering the entire path from raw sensor data to real-world decisions.
